1 link tagged with all of: architecture + hallucination + inference + small models
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The author argues that you don't need massive AI models for most real-world applications—smaller models handle instruction-following well and fail predictably when they don't know something, rather than confidently making things up like larger models do. He's building a system that lets you swap between different AI providers through a single interface.
- Small models hallucinate less reliably than large ones; when told clearly that information is missing, they admit it instead of inventing plausible-sounding answers
- Larger models produce more polished fabrications, making their hallucinations harder to catch in production
- The author's architecture (Pepper) uses a provider-neutral layer that treats adding new AI backends as simple routing and translation work, not a full rewrite